Abstract:

A system for touchpoint content action customization at a current
touchpoint to achieve a business objective includes a user touchpoint
data capture unit and a content action optimization engine. The content
action optimization engine is configured to select a plurality of
candidate content actions for the current touchpoint based on content
action metadata, to determine an observed percentage of success for an
observed user behavior for each of the plurality of candidate content
actions based on the user group to which the user belongs, and to
determine a customized content action of the plurality of candidate
content actions to implement at the current touchpoint to achieve the
business objective that has the highest observed percentage of success.

Claims:

1. A system for touchpoint content action customization at a current
touchpoint to achieve a business objective, comprising:a user touchpoint
data capture unit configured to receive user data, said user data
including user attributes for a user visiting the current touchpoint, and
to determine a user group to which the user belongs based on the user
attributes of the user; anda content action optimization engine
configured to select a plurality of candidate content actions from a
content action repository for the business objective and for the current
touchpoint based on content action metadata, wherein said content action
metadata describes the business objective for which the content action is
used and the touchpoint for which the content action is used, to
determine an observed percentage of success for an observed user behavior
for each of the plurality of candidate content actions based on the user
group to which the user belongs, and determine a customized content
action of the plurality of candidate content actions to implement at the
current touchpoint to achieve the business objective that has the highest
observed percentage of success.

2. The system of claim 1, wherein the content action optimization engine
is configured to implement the customized content action at the current
touchpoint.

3. The system of claim 2, wherein the content action optimization engine
is configured to record a user behavior in response to implementing the
customized content action at the current touchpoint.

4. The system of claim 3, wherein the content action optimization engine
is configured to compare the business objective to the recorded user
behavior to determine if the business objective has been achieved.

5. The system of claim 4, wherein the content action optimization engine
is configured to determined that the business objective has been achieved
when the business objective is equivalent to the recorded user behavior.

6. The system of claim 5, wherein the content action optimization engine
is configured to determine another customized content action for a next
touchpoint if the business objective is not equivalent to the recorded
user behavior.

7. The system of claim 1, wherein the content action comprises at least
one of a tactic, a strategy, a seminar, a button, a product presentation
or demonstration, a product catalog, product pricing, information about a
product, a social media piece, and a set of frequently asked questions.

8. The system of claim 1, wherein the content action metadata for each
content action further identifies each content action, describes each
content action, and describes how each content action is used.

9. The system of claim 1, wherein the content action metadata for each
content action further identifies constraints for each content action's
use.

10. A method for touchpoint content action customization at a current
touchpoint to achieve a business objective, said method
comprising:receiving user data including user attributes for a user
visiting the current touchpoint;determining a user group to which the
user belongs based on the user attributes of the user;selecting, using a
processor, a plurality of candidate content actions from a content action
repository for the business objective and for the current touchpoint
based on content action metadata, wherein said content action metadata
describes the business objective for which the content action is used and
the touchpoint for which the content action is used;determining an
observed percentage of success for an observed user behavior for each of
the plurality of candidate content actions based on the user group to
which the user belongs; anddetermining a customized content action of the
plurality of candidate content actions to implement at the current
touchpoint to achieve the business objective that has the highest
observed percentage of success.

11. The method of claim 10, further comprising:implementing the customized
content action at the current touchpoint.

12. The method of claim 11, further comprising:recording the behavior of
the user in response to implementing the customized content action at the
current touchpoint.

13. The method of claim 12, further comprising:comparing the business
objective to the recorded user behavior; andif the business objective is
not equivalent to the recorded user behavior, repeating the selecting,
determining an observed percentage of success, and determining a
customized content action steps.

14. The method of claim 10, wherein the content action comprises at least
one of a tactic, a strategy, a seminar, a button, a product presentation
or demonstration, a product catalog, product pricing, information about a
product, a social media piece, and a set of frequently asked questions.

15. The method of claim 10, wherein the content action metadata for each
content action further identifies constraints for each content action's
use.

16. A computer readable medium having stored thereon a computer executable
program for touchpoint content action customization at a current
touchpoint to achieve a business objective, the computer executable
program when executed causes a computer system to:receive user data
including user attributes for a user visiting the current
touchpoint;determine a user group to which the user belongs based on the
user attributes for the user;select a plurality of candidate content
actions from a content action repository for the business objective and
for the current touchpoint based on content action metadata, wherein said
content action metadata describes the business objective for which the
content action is used and the touchpoint for which the content action is
used;determine an observed percentage of success for an observed user
behavior for each of the plurality of candidate content actions based on
the user group to which the user belongs; anddetermine a customized
content action of the plurality of candidate content actions to implement
at the current touchpoint to achieve the business objective that has the
highest observed percentage of success.

17. The computer readable medium of claim 16, further
comprising:implementing the customized content action at the current
touchpoint.

18. The computer readable medium of claim 17, further comprising:recording
a user behavior in response to implementing the customized content action
at the current touchpoint.

19. The computer readable medium of claim 18, further comprising:comparing
the business objective to the recorded user behavior; andif the business
objective is not equivalent to the recorded user behavior, repeating the
selecting, determining an observed percentage of success and determining
a customized content action steps.

20. The computer readable medium of claim 16, wherein the content action
comprises at least one of a tactic, a strategy, a seminar, a button, a
product presentation or demonstration, a product catalog, product
pricing, information about a product, a social media piece, and a set of
frequently asked questions.

Description:

PRIORITY

[0001]This application claims priority to U.S. provisional patent
application Ser. No. 61/169,892, filed on Apr. 16, 2009, and entitled
"Digital Platform", which is incorporated by reference in its entirety.

BACKGROUND

[0002]The Internet has become increasingly popular with the consuming
public and web pages on the Internet are considered powerful media for
advertising. Advertisements on web pages are directly linked to the web
pages as fixed inline images, while more flexible systems allow a
separation of advertisement selection and placement, but offer only a
random selection mechanism. Many of the methods implemented by
advertisers are typically too simple to take advantage of the
just-in-time selection and delivery process available with web page
advertisements. Although conventional filtering techniques allow for
precise targeting of the advertisements, the task of selecting whom to
target what advertisement are left to largely to the advertiser. This
requires extended efforts on the advertiser side, who has to rely on
countless statistics and demographic studies.

BRIEF DESCRIPTION OF DRAWINGS

[0003]The embodiments of the invention will be described in detail in the
following description with reference to the following figures.

[0004]FIG. 1 illustrates a system for touchpoint content action
customization, according to an embodiment;

[0005]FIG. 2A illustrates an example of determining candidate content
actions, according to an embodiment;

[0006]FIG. 2B illustrates an example of determining a customized content
action, according to an embodiment;

[0007]FIG. 2c illustrates an additional example of determining a
customized content action, according to an embodiment;

[0008]FIG. 3 illustrates a tree structure, according to an embodiment;

[0009]FIG. 4 illustrates a method for touchpoint content action
customization, according to an embodiment; and

[0010]FIG. 5 illustrates a block diagram of a computing system, according
to an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

[0011]For simplicity and illustrative purposes, the principles of the
embodiments are described by referring mainly to examples thereof. In the
following description, numerous specific details are set forth in order
to provide a thorough understanding of the embodiments. It will be
apparent however, to one of ordinary skill in the art, that the
embodiments may be practiced without limitation to these specific
details. In some instances, well known methods and structures have not
been described in detail so as not to unnecessarily obscure the
embodiments. Also, the embodiments described herein may be used with each
other in various combinations.

1. Overview

[0012]According to an embodiment of the invention, customized content
actions are provided to a user at multiple touchpoints the user visits
for a customized end-to-end user experience. A customized content action
is content that is presented and/or an action that is performed. The
content or action is customized based on a user and their previous
interactions and other information. Examples of a customized content
action may include a tactic, a strategy, a seminar, a button, a product
presentation or demonstration, a product catalog, product pricing,
information about a product, a social media piece, frequently asked
questions presented to the user as additional information, etc.

[0013]As used herein, a "touchpoint" is a specific interaction between an
entity and a user within a specific channel. An entity may be a company,
another user or some other type of entity. A channel is a medium for
providing one or more touchpoints. Examples of channels include the
Internet, TV, radio, etc. In instances where the channel is the Internet,
examples of touchpoints may be a webpage or a portion of a webpage with
which the user interacts.

[0014]The customized content action provided to the user at each
touchpoint is based on dynamic desired-outcome driven optimization. Thus,
the system dynamically presents a customized content action to a user at
each touchpoint the user visits that is driven by a desired-outcome, such
as a business objective. The business objective may include selling a
particular product to a user, directing the user to subscribe to a
specific service, etc. Thus, a user is funneled through various
touchpoints, each with a customized content action, in a customized
end-to-end user experience to achieve the business objective. The system
provides an enhanced automated content action selection process to
provide the user with a customized user display.

2. System

[0015]FIG. 1 illustrates a system 100 for content action customization,
according to an embodiment. As shown therein, the system 100 includes a
user touchpoint data capture unit 140, a user touchpoint database 150, a
content action optimization engine 160, a content action repository 170,
and content action optimization model 180. It should be understood that
the system 100 depicted in FIG. 1 may include additional components and
that some of the components described herein may be removed and/or
modified without departing from a scope of the system 100.

[0016]Users 110a-n access touchpoints 120a-n of a specific channel 115.
For example, the channel 115 is the Internet and the touchpoints 120a-n
are web site touchpoints. The users 110a-n may access the web site
touchpoints 120a-n through end user devices connected to the Internet,
such as, computers, laptops, cellular telephones, personal digital
assistants (PDAs), etc. According to an embodiment, when the users 110a-n
access the web site touchpoints 120a-n, the system 100 captures user data
130. For example, the user touchpoint data capture unit 140 captures the
user data 130 at each of the one or more touchpoints 120a-n that the user
110a accesses or visits. The user touchpoint data capture unit 140 may
capture the user data 130 from HTML or Javascript embedded in the
touchpoint 120a-n, from an agent running on a user device, from third
party sources collecting user information, etc. The captured user data
130 may include historical data about the course of interaction at the
touchpoints 120a-n already visited by the user, actions taken by the user
and user attributes, such as gender, geographic location, purchase
habits, etc.

[0017]As shown in FIG. 1, the user touchpoint data capture unit 140 stores
the captured user data 130 in the user touchpoint database 150. In
addition, the content action optimization engine 160 is depicted as
receiving the user data 130 from the user touchpoint database 150 and
candidate content actions 195 from the content action repository 170.
Moreover the content action optimization engine 160 is depicted as
receiving a business objective 190. As discussed in greater detail herein
below, the content action optimization engine 160 is generally configured
to use the user data 130 as well as the content action optimization model
180 and business objective 190 to determine a customized content action
198 for each of the touchpoints 120a-n visited by the user 110a.

[0018]The content action optimization model 180 includes historic
information regarding resulting user behavior in response to various
content actions presented to a type or segment of users having particular
user attributes at specific touchpoints 120a-n. In one example, the
content action optimization model 180 includes user data grouped by
attributes, touchpoints visited, content actions presented at the
touchpoints and observed user behavior. For example, one group may
include Asian women between 40 and 50 years. An observed user behavior
for the group may include that they purchased handbags priced over
$150.00 55% of the time when presented with a certain content action at a
certain touchpoint. Thus, the content action optimization model 180 may
include many different types of observed behavior for many different
groups of users for different touchpoints, and this observed behavior may
be used to estimate or predict behavior for various touchpoints and
users. According to an embodiment, therefore, the content action
optimization model 180 may be generated based on the analysis of observed
user behavior and/or based on the analysis of historic data provided by
external data sources.

[0019]Generally speaking, a company may input the business objective 190
to be achieved into the content action optimization engine 160. For
example, the business objective 190 may include selling a particular
product to a user, directing a user to subscribe to a specific service,
or any other desired business outcome.

[0020]Based on the inputs, for instance, the content action optimization
model 180, the user data 130 and the business objective 190, the content
action optimization engine 160 is configured to dynamically determine the
customized content action 198 to implement at a particular touchpoint
120a. By way of example, a plurality of content actions, which may
include various tactics, strategies, seminars, buttons, product
presentations or demonstrations, product catalogs, product pricing,
information about products, social media pieces, frequently asked
questions, etc., are stored in the content action repository 170.

[0021]The content action repository 170 also includes metadata associated
with each content action, which identifies each content action, describes
each content action, and describes how each content action is used. The
metadata also includes constraints for each content action, which
describes restrictions on the use of the content action, which may be in
the form of descriptors, instructional videos, etc. The constraints may
describe at which touchpoint the content action may be implemented and
for which business objective the content action may be used. For example,
a specific content action may only be used for a specific touchpoint or
for a specific segment of the population. According to an embodiment, the
content actions are grouped according to corresponding business
objectives and touchpoints based on the content action metadata.

[0022]The content action optimization engine 160 determines which
customized content action to implement at a particular touchpoint. For
example, the user 110a accesses the particular touchpoint 120a, which
comprises a web page on the Internet. In order to determine the
customized content action 198 to implement at the touchpoint 120a for the
user 110a, the content action optimization engine 160 retrieves candidate
content actions 195 from the content action repository 170. Note that in
some instances the content action optimization engine 160 retrieves a
single candidate content action 195. The candidate content actions 195
are retrieved based on the particular touchpoint 120a that the user 110a
is visiting, as well as, the business objective 190 for which the content
action is to be used. Thus, the candidate content actions 195 are
retrieved based on the metadata of the content actions in the content
action repository 170.

[0023]In one example, the metadata for the content actions are compared to
current touchpoint information for a user to select the candidate content
actions 195. For example, as shown in FIG. 2A, the content action
repository 170 includes the content actions listed in table 210. As shown
in the table 210, content actions A, B, and C are retrieved as the
candidate content actions 195 because the user is at touchpoint 120a and
the business objective 190 is business objective 1. More particularly,
the content actions A, B, and C may be selected and retrieved as the
candidate content actions 195 based upon information contained in the
metadata for the content actions A, B and C. In contrast, the metadata
for content actions D-J describe the content actions D-J as either not
being used for touchpoint 120a or not being for business objective 1.

[0024]Once the candidate content actions 195 are retrieved from the
content action repository 170, the content action optimization engine 160
may select one of the candidate content actions 195 as the customized
content action 198 to be implemented at the touchpoint 120a. In one
embodiment, the customized content action 198 is the candidate content
action that is most likely to achieve the business objective 1. In one
example, to determine the customized content action 198, the content
action optimization engine 160 identifies a user group to which the user
110a belongs by matching the user attributes for the user 110a stored in
the user data 130 to the user group data in the optimization model 180.
For example, the content action optimization model 180 includes data
grouped by user groups. Each user group has a corresponding set of
attributes that can be matched to user attributes. Each user group in the
optimization model 180 may have associated categories including
touchpoint visited, content action presented at the touchpoint and
observed user behavior. Then, based on the user group to which the user
110a belongs, the content action optimization engine 160 identifies each
of the candidate content actions 195 in the determined user group. The
data associated with the identified content actions within the user group
include an observed percentage of success at achieving the business
objective. In addition, the content action optimization engine 160
analyzes the data associated with each of the identified content actions
in the content action optimization model 180 and may select the candidate
content action that has the highest percentage of the observed percentage
of success at achieving the business objective as the customized content
action 198 to implement for the user 110a at the touchpoint 120a.
According to another embodiment, the content action optimization engine
160 uses different weighting schemes to select the customized content
action 198.

[0025]FIG. 2B illustrates an example of information contained in the
content action optimization model 180 for a single user group 221, shown
as Asian Women in the age range of 40-50 years. For example, the user
attributes in the captured user data 130 for the user 110a are compared
with the user groups in the content action optimization model 180. If the
user 110a is a 44-year old Asian woman, then the content action
optimization engine 160 uses the subset of data in the content action
optimization model 180 for the user group 221 of Asian women between 40
and 50. The user group 221 is part of a user group data subset in the
content action optimization model 180 and includes content actions for
several touchpoints and percentages of achieving the business objective
190 for each content action, as shown in table 220 in FIG. 2B. Based on
the subset of data in the content action optimization model 180, the
content action A has an observed behavior percentage of 50%, the content
action B has an observed behavior percentage of 20% and the content
action C has an observed behavior percentage of 30%. Thus, the identified
content action of the candidate content actions 195 with the highest
observed behavior percentage is the content action A at 50%, and
therefore the content action A is the customized content action 198, as
shown in FIG. 2c. The customized content action 198 is then implemented
at touchpoint 120a for user 110a.

[0026]The user data 130 for user 110a is then updated with data regarding
the customized content action 198 that was implemented at touchpoint 120a
and the user data 130 is again saved in the user touchpoint database 150.

[0027]The user 110a then may continue to the next touchpoint 120b. At
touchpoint 120b, a new customized content action to implement at
touchpoint 120b for user 110a is determined based on the same steps noted
above, based on the additional data saved with the captured user data 130
including which content action was presented beforehand at each
touchpoint visited by the user 110a, and continues until the business
objective 190 is achieved. Thus, the user 110a is funneled through a
plurality of touchpoints 120a-n in which a customized content action is
presented at each touchpoint aimed to achieve the business objective 190,
until the business objective 190 is achieved.

[0028]According to an embodiment, the candidate content actions 195 are
branches of a tree structure. At each touchpoint, a new tree structure of
candidate content actions 195 is formed since, at each touchpoint,
updated user data is captured including the last touchpoint visited data
and user attributes. For example, in FIG. 3, at touchpoint 120a, three
branches are shown as 310, 320 and 330. Each branch 310, 320 and 330,
corresponds to the same user group which is determined based on user
attributes as discussed above. Each branch 310, 320 and 330, is further
distinguished from each other based on the business objective to which
the content action sub-branches pertain. For example, for each branch
310, 320 and 330, a variety of candidate content actions 195 (A-Z) are
shown. For business objective 1 and user group 221 listed as branch 310,
content actions A, B and C are shown as sub-branches 340, 350 and 360,
respectively. An observed user behavior and a percentage of observed user
behavior success is shown for each content action sub-branch 340, 350 and
360. For example, for content action sub-branch 340, the "Observed User
Behavior" is "Buy Purse" and the "Percentage" is "50%". Thus, 50% of the
time, when content action A listed as 340 is implemented at touchpoint
120a, the user in user group 221 buys the purse. Thus, the tree structure
formed at each touchpoint changes according to user attributes, last
touchpoint visited, last content action presented, content action
metadata, etc. In addition, the user is funneled through a plurality of
touchpoints in which a new tree structure is formed at each touchpoint
aimed to achieve the business objective, until the business objective is
achieved.

3. Method

[0029]FIG. 4 illustrates a flowchart of a method 400 for content action
customization at a touchpoint, according to an embodiment. It should be
understood that the method 400 depicted in FIG. 4 may include additional
steps and that some of the steps described herein may be removed and/or
modified without departing from a scope of the method 400. In addition,
the method 400 may be implemented by the system 100 described above with
respect to FIG. 1 by way of example, but may also be practiced in other
systems.

[0030]At step 410, the system 100 receives input of a business objective
190. The business objective may be a business objective received from a
company. For example, the business objective may be to sell a product or
service.

[0031]At step 420, the system 100 captures user data of a user visiting
the touchpoint. The system 100 may capture the user data from HTML or
Javascript embedded in the touchpoint, from an agent running on a user
device, from third party sources collecting user information, etc. The
captured user data may include historical data about the course of
interaction at the touchpoints already visited by the user, actions taken
by the user and user attributes, such as gender, geographic location,
purchase habits, etc. In addition, the captured user data is stored in
the user touchpoint database and is used as input for the system 100, as
is further described below.

[0032]At step 430, the system 100 selects and retrieves one or more
candidate content actions 195. Based on the content action optimization
model 180 as described above with regard to the system 100, the captured
user data and the input business objective, the system 100 dynamically
determines the candidate content actions from a plurality of content
actions are stored in the content action repository 170. The plurality of
content actions may include a tactic, a strategy, a seminar, a button, a
product presentation or demonstration, a product catalog, product
pricing, information about a product, a social media piece, frequently
asked questions, etc. The content action repository 170 also includes
metadata associated with each content action identifying each content
action, describing each content action and describing how each content
action is used. The content action repository 170 further includes
constraints for each content action describing restrictions on the use of
the content action, which may be in the form of descriptors,
instructional videos, etc. The constraints may describe at which
touchpoint the content action can be implemented and for which business
objective the content action can be used. For example, a specific content
action may only be used for a specific touchpoint or for a specific
segment of the population. The content actions in the content action
repository are grouped according to corresponding business objectives and
touchpoints based on the content action metadata. The candidate content
actions are retrieved based on the touchpoint the user is currently
visiting and based on the business objective for which the content action
may be used. Thus, the candidate content actions are retrieved based on
the metadata of the content actions in the content action repository.

[0033]At step 440, once the candidate content actions are retrieved from
the content action repository, the system 100 selects the customized
content action to be implemented at the touchpoint. In one embodiment,
the customized content action is the candidate content action that is
most likely to achieve the business objective. For example, to determine
the customized content action, the content action optimization engine 160
identifies a user group to which the user belongs by matching the user
attributes for the user stored in the user data to the user group data in
the optimization model 180. Then, based on the user group to which the
user belongs, the system 100 identifies each of the candidate content
actions in the determined user group. The system 100 analyzes the data
associated with each of the identified content actions in the content
action optimization model, in which the data associated with the
identified content actions within the user group include an observed
percentage of success at achieving the business objective. The system 100
may select the candidate content action that has the highest percentage
of the observed percentage of success for the business objective as the
customized content action to implement for the user at the touchpoint.

[0034]In step 450, the determined customized content action is implemented
at the touchpoint.

[0035]In step 460, a decision is made whether the business objective has
been achieved. If the customized content action implemented at the
touchpoint produces the observed behavior that is equivalent to the
business objective, the process moves on to step 470 where the method 400
is ended. However, if the customized content action implemented at the
touchpoint does not produce the observed behavior that is equivalent to
the business objective, the user moves on to the next touchpoint and the
process restarts at step 420. At step 470, regardless of whether the
business objective has been achieved, the captured user data is updated
with data regarding the customized content action that was implemented at
step 450. The user data is again saved.

[0036]FIG. 5 shows a computer system 500 that may be used as a hardware
platform for the creative marketplace system 100. The computer system 500
may be used as a platform for executing one or more of the steps,
methods, and functions described herein that may be embodied as software
stored on one or more computer readable storage devices, which are
hardware storage devices.

[0037]The computer system 500 includes a processor 502 or processing
circuitry that may implement or execute software instructions performing
some or all of the methods, functions and other steps described herein.
Commands and data from the processor 502 are communicated over a
communication bus 504. The computer system 500 also includes a computer
readable storage device 503, such as random access memory (RAM), where
the software and data for processor 502 may reside during runtime. The
storage device 503 may also include non-volatile data storage. The
computer system 500 may include a network interface 505 for connecting to
a network. It will be apparent to one of ordinary skill in the art that
other known electronic components may be added or substituted in the
computer system 500.

[0038]While the embodiments have been described with reference to
examples, those skilled in the art will be able to make various
modifications to the described embodiments without departing from the
scope of the claimed embodiments. Also, the embodiments described herein
may be used to determine which content actions are undesirable, which
content actions to implement that receive the most online traffic, etc.